# MCP Connectors

Servers exposing tools, data and prompts over the Model Context Protocol so any compatible client can use them.

**Rests on:** Agent Cards

**Leads to:** Business Workflow Integration

*AI Agents · Model Context Protocol*


## Why learn this

Mastering MCP connectors lets you build AI assistants that can actually do things: pull live data, use external programs, and act in the real world. By understanding how these connections work, you write an integration once and have it work across every compatible AI program, from ChatGPT-style assistants to custom frameworks. This skill is essential because without it, an assistant is just a chat box; with it, the assistant can book flights, query databases, or control devices. It also introduces the critical question of safety: when you let an AI talk to your private systems, where is the line between safe interaction and risky access?


## The intuition

Think of an MCP connector as a universal adapter for AI applications. Have you ever bought a device that only works with one brand of charger? An MCP connector solves this by providing a standard way for different AI programs to share capabilities. Instead of building a separate connection for every AI app you use—one for ChatGPT, one for your local assistant, one for your enterprise chatbot—you build a small server that connects to any app that speaks the same language. This server exposes three main things the AI can use: tools (like 'search the web'), data (like your calendar entries), and prompts (like 'summarize this article'). Any compatible client program can discover these items and use them. So if you build a server that gives an AI access to your company's inventory, every AI tool in your company can now query that inventory without any extra work from you. That's the core benefit: write once, use everywhere.


## An analogy

Imagine you run a small restaurant and you install a universal ordering system for multiple delivery apps. You create a standardized card that lists what you offer: dishes (tools), ingredients (data), and cooking instructions (prompts). Every delivery app reads this same card. One app sees 'Chicken Curry – available', another sees 'Ingredients: chicken, curry leaves'. They all contact your kitchen using the same protocol, so you don't have to negotiate separate agreements with each app. A caveat: unlike a physical menu that stays static, your tool's description can change while the apps are still looking at it, which means an app might expect one thing but get another if you update your offerings without warning.


## Definition

An MCP connector is a server that exposes tools, data, and prompts using the Model Context Protocol (a standard set of rules for how AI programs communicate), allowing any compatible program to discover and use them without needing to know how they are built internally.


## How it connects

This concept extends Tool Use and Function Calling — if you understand how an AI calls a specific operation rather than just generating text, an MCP connector is the standardized way to make those operations available across different systems. In your learning path, you have mastered calling functions, and now you are learning the architecture that makes those calls scalable over a network. It connects directly to AI Agents (an agent uses these connectors to gather information and take actions) and AI Security (each connector extends the trust boundary, which is the limit of where an AI is allowed to access your private data). It also relates to Claude Skills and Business Workflow Integration; think of a skill as a high-level behavior you assign to the AI, while the connector is the low-level plumbing that makes that behavior possible.


## From scratch, in Python

```python
import json

def handle_request(request):
    # request is a dictionary with 'method' and 'params'
    method = request.get('method')
    if method == 'list_tools':
        # Return a list of available tools
        tools = [
            {'name': 'add', 'parameters': {'a': 'number', 'b': 'number'}},
            {'name': 'get_weather', 'parameters': {'city': 'string'}}
        ]
        return {'tools': tools}
    elif method == 'call_tool':
        # parameters = request['params']['arguments']
        tool_name = request['params']['name']
        args = request['params']['arguments']
        if tool_name == 'add':
            result = args['a'] + args['b']
            return {'result': result}
        elif tool_name == 'get_weather':
            # Dummy implementation
            return {'result': 'Sunny 25C'}
        else:
            return {'error': 'Tool not found'}
    else:
        return {'error': 'Method not supported'}

# Simulate an incoming request from a client
request = {'method': 'call_tool', 'params': {'name': 'add', 'arguments': {'a': 3, 'b': 5}}}
response = handle_request(request)
print(json.dumps(response))

```


## What the code does

The plain Python code shows the essence of an MCP server: it receives a request with a method name and parameters, and it returns a response. The 'handle_request' function acts like a mini router — it checks if the client is asking for a list of tools or to call a specific tool. For 'add', it pulls out the 'a' and 'b' values from the arguments and adds them. This is exactly what the framework code does, but with all the protocol details handled for you. The framework version uses decorators (@app.list_tools()) to register handlers, and it wraps the response in MCP types. Under the hood, the framework does the same JSON processing you see in the plain version, but it also handles network transport, error handling, and standards compliance.


## Common misconceptions

**MCP connectors are only for chatbots** — MCP connectors are only for chatbots.

While chatbots are a common use case, MCP connectors can be used by any AI system that needs to access external data or tools — including automated workflows, data pipelines, and enterprise systems. In fact: MCP connectors are a general standard for AI integration, applicable wherever a model needs to interact with the external world.

**MCP is a programming library** — MCP is just a programming library I can install with pip.

MCP is a protocol — a set of rules for how clients and servers communicate. Libraries like the Python SDK implement this protocol, but the protocol itself is language-agnostic. In fact: MCP is a standards-based protocol; libraries make it easier to implement, but they are not the protocol itself.

**Writing an MCP connector is a one-way street** — Once I write an MCP connector, it's done and safe forever.

The connector's tools, data, and prompts are exposed over a network, and their definitions can change — deliberately or maliciously — after approval, posing security risks. In fact: Connectors require ongoing governance: versioning, security audits, and monitoring to ensure they remain trustworthy.

**Each AI app needs its own connector** — I have to build a separate connector for each AI application I use.

The whole point of MCP is to standardize the interface — write once, use everywhere. In fact: A single MCP server can be used by any compatible client, saving significant development effort.

**MCP only works with large models** — MCP connectors are overkill for small AI models on my phone.

Small models can still benefit from MCP connectors — they can offload heavy tasks to a more capable server or fetch external data, making them more useful. In fact: MCP is valuable across model sizes; it's about connectivity, not model capability.


## In short

An MCP connector is a standard, reusable way to expose AI capabilities to any client. It lets you write an integration once and use it everywhere, but each connector expands your system's attack surface. You must manage tool definitions carefully — they can change after approval, so integrity and versioning are key. Master this and you open the door to building agents that can truly act in the world.


## Where this is used

MCP connectors are the backbone of modern agentic AI. In large language models like GPT-4 or Claude, a connector might expose a web search tool, allowing the model to fetch current information rather than relying on old training data. For small models on phones, connectors let them query a home server for calendar or health data without having that data stored on the device. In retrieval-augmented generation (RAG), connectors are used to plug into vector databases — the AI sends a query, the connector returns relevant documents, and the AI generates an answer with that context. In agentic systems, connectors are the tool-calling interface: the agent plans a sequence of steps, and each step involves calling a connector to execute an action like 'send email' or 'book appointment'. In everyday applied ML, connectors are used in recommendation systems to fetch user preferences from a backend, in fraud detection to query transaction databases, and in speech systems to retrieve user profiles. A developer using MCP typically writes a server that lists its tools and implements their logic, then runs it with a simple command. The client (the AI app) automatically discovers these tools and can call them. What breaks when it's done badly? If the connector exposes data without proper authentication, it becomes a security hole — each connector extends the trust boundary of the whole system. If tool definitions change after approval without versioning, the client may call an outdated tool and get errors. That's why integrity and careful management of tool definitions are critical — as your library notes warn, 'Tool definitions can change after approval, which is the rug-pull risk specific to this model.' A well-designed connector is secure, versioned, and clearly documented.


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Read this on the web: https://basavapurushottam.com/learn-ai/mcp-connectors
